Gender classification in smartphones using gait information. (1st March 2018)

• Record Type: Journal Article
• Authors: Jain, Ankita; Kanhangad, Vivek
• Is Part Of: Expert systems with applications. Volume 93(2018)
• Page Start: 257
• Page End: 266
• Publication Date: 2018-03-01
• Subjects: Gait biometrics -- Gender recognition -- Accelerometer -- Gyroscope -- Histogram of gradient; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.10.017
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel approach for gender classification in smartphones using gait information. Gait information collected from accelerometer and gyroscope sensors is utilized. Feature extraction from gait cycles using histogram of gradient descriptor. The dataset consists of a total of 654 gait data acquired from 109 subjects. Results show that proposed approach provides higher accuracy than existing methods. Abstract: Gender classification in smartphones has a lot of potential applications. Specifically, the gender information can be used by expert and intelligent systems that are part of healthcare, smart spaces and biometric-based access control applications. For example, operations of intelligent systems in a smart space can be customized based on gender information to provide an enhanced user experience. Similarly, a biometric system can use gender as a soft biometric trait to improve its user authentication performance. This paper presents an approach for gender classification using users' gait information captured using the built-in sensors of a smartphone. Histogram of gradient (HG) method is proposed to extract features from the gait data, which includes a set of signals collected from accelerometer and gyroscope sensors of a smartphone. The bootstrap aggregating classifier utilizes the discriminatory information in these features for classification of the gender. The performance of the proposed approach has been evaluated on datasets collected using two different smartphones. These datasets contain a total of 654 gait data from 109 subjects. Our experimental results show that the classification accuracy of the proposed approach is higher than that of the existing methods. Additional experiments performed to examine the effect of variations in walking speed indicate that these variations have a minimal impact on the performance of proposed approach. Furthermore, results from our experiments performed on the gait data collected using two different smartphones suggest that the performance of the proposed algorithm for gender recognition is consistent across the two datasets, achieving classification accuracies of 91.78%, 94.44% and 88.89% on the first dataset and 90.48%, 91.07% and 88.46% on the second dataset for normal, fast and slow walking speeds, respectively. The results of this study are significant as they indicate that gait information captured by the smartphones' built-in sensors can be used to derive gender information reliably and unobtrusively.